Gépi tanulás, statisztikus adatfeldolgozás, jelfeldolgozáson alapuló alkalmazások
(pl. beszéd, kép, videó)
Mesterséges intelligencia, intelligens rendszerek, multi-ágens rendszerek
Neurális képalkotás és neurális számítástudomány
Identity tracking and instance segmentation are crucial in several areas of biological
research. Behavior analysis of individuals in groups of similar animals is a task
that emerges frequently in agriculture or pharmaceutical studies, among others. Automated
annotation of many hours of surveillance videos can facilitate a large number of biological
studies/experiments, which otherwise would not be feasible. Solutions based on machine
learning generally perform well in tracking and instance segmentation; however, in
the case of identical, unmarked instances (e.g., white rats or mice), even state-of-the-art
approaches can frequently fail. We propose a pipeline of deep generative models for
identity tracking and instance segmentation of highly similar instances, which, in
contrast to most region-based approaches, exploits edge information and consequently
helps to resolve ambiguity in heavily occluded cases. Our method is trained by synthetic
data generation techniques, not requiring prior human annotation. We show that our
approach greatly outperforms other state-of-the-art unsupervised methods in identity
tracking and instance segmentation of unmarked rats in real-world laboratory video
recordings.
